Half Open Multi-Depot Heterogeneous Vehicle Routing Problem for Hazardous Materials Transportation

How to reduce the accidents of hazardous materials has become an important and urgent research topic in the safety management of hazardous materials. In this study, we focus on the half open multi-depot heterogeneous vehicle routing problem for hazardous materials transportation. The goal is to determine the vehicle allocation and the optimal route with minimum risk and cost for hazardous materials transportation. A novel transportation risk model is presented considering the variation of vehicle loading, vehicle types, and hazardous materials category. In order to balance the transportation risk and the transportation cost, we propose a bi-objective mixed integer programming model. A hybrid intelligent algorithm is developed based on the ε-constraint method and genetic algorithm to obtain the Pareto optimal solutions. Numerical experiments are provided to demonstrate the effectiveness of the proposed model. Compared with the close multi-depot heterogeneous vehicle routing problem, the average risk and cost obtained by the proposed bi-objective mixed integer programming model can be reduced by 3.99% and 2.01%, respectively. In addition, compared with the half open multi-depot homogeneous vehicle routing problem, the cost is significantly reduced with the acceptable risk

Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm

To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy improved sparrow search algorithm (MISSA) to optimize a BP neural network. Three strategies, namely Bernoulli chaotic mapping, inertial weights, and t-distribution, are introduced into the sparrow search algorithm to improve its convergent speed and accuracy. Using the Bernoulli chaotic map to process the population of sparrows to enhance its population richness, the weight is introduced into the updated position of the sparrow to improve its search ability. The adaptive t-distribution is used to interfere and mutate some individual sparrows to make the algorithm reach the optimal solution more quickly. The initial parameters of the BP neural network were optimized using the improved sparrow search algorithm to obtain the optimized MISSA-BP antenna surrogate model. This model is combined with multi-objective particle swarm optimization (MOPSO) to solve the design problem of the multi-objective antenna and verified by a triple-frequency antenna. The simulated results show that this method can predict the performance of the antennas more accurately and can also design the multi-objective antenna that meets the requirements. The practicality of the method is further verified by producing a real antenna

受贿的经济分析 = An economic analysis of bribery

This paper points out some precautionary measures, including the improving of incentives, the increasing of bribery cost and the deepening of political, economic and monitoring mechanism, in an effort to form an precautionary network efficient in containing the high crime rate of bribery.​Master of Science (Managerial Economics

Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm

To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy improved sparrow search algorithm (MISSA) to optimize a BP neural network. Three strategies, namely Bernoulli chaotic mapping, inertial weights, and t-distribution, are introduced into the sparrow search algorithm to improve its convergent speed and accuracy. Using the Bernoulli chaotic map to process the population of sparrows to enhance its population richness, the weight is introduced into the updated position of the sparrow to improve its search ability. The adaptive t-distribution is used to interfere and mutate some individual sparrows to make the algorithm reach the optimal solution more quickly. The initial parameters of the BP neural network were optimized using the improved sparrow search algorithm to obtain the optimized MISSA-BP antenna surrogate model. This model is combined with multi-objective particle swarm optimization (MOPSO) to solve the design problem of the multi-objective antenna and verified by a triple-frequency antenna. The simulated results show that this method can predict the performance of the antennas more accurately and can also design the multi-objective antenna that meets the requirements. The practicality of the method is further verified by producing a real antenna

Chemically modified graphite felt as an efficient cathode in electro-fenton for p-nitrophenol degradation

International audienceA simple method with low-cost chemical reagents ethanol and hydrazine hydrate was used to modify graphite felt as the cathode for electro-fenton (EF) application, using p-nitrophenol (p-Np) as the model pollutant. Characterized by scanning electron microscope, contact angle, Raman spectrum and X-ray photoelectron spectroscopy, the morphology and surface physicochemical properties after modification were observed considerably changed. After modification, some nanoparticles and oxygen and nitrogen-containing functional groups appeared on the cathode surface, which greatly improved the surface hydrophilic property and the electrocatalytic activity for oxygen reduction reaction. The effects led to the hydrogen peroxide accumulation on the modified cathode markedly increased to 175.8 mg L−1, while that on the unmodified one was only 67.5 mg L−1. p-Np of initial 50 mg L−1 could be completely removed by EF using the modified cathode, and the mineralization ratio reached 51.4%, more than 2 times of the pristine one. After 10 cycles, the mineralization ratio of the modified cathode was still above 45%, suggesting that the modification method can provide an effective approach to improve EF performance, and thus benefits to promote its environmental applications

Mechanical Properties and In Vitro Corrosion Behaviors of Biodegradable Magnesium Alloy Suture Anchors

Biodegradable suture anchors based on Mg-Nd-Zn-Zr alloy were developed for ligament-to-bone fixation in rotator cuff surgeries. The Mg alloy anchors were designed with structural features of narrow tooth and wide tooth, and simulated through finite element analysis (FEA). Meanwhile, the corrosion behaviors of the Mg alloy anchors were studied by immersion test and the mechanical properties were investigated by measuring the maximum torque and pull-out force. The simulation result showed that the wide-tooth anchor exhibited more a uniform stress distribution and lower shear stress in the torsion process, suggesting a satisfactory torsional resistance of this structure. Meanwhile, the wide-tooth anchor exhibited a lower Von-Mises stress after applying the same pull-out force in the simulation, indicating a higher resistance to pull-out failure of the anchor. The result of the immersion test indicated that the wide-tooth anchor exhibited a slightly slower corrosion rate in Hank’s solution after 14-day immersion, which was beneficial to enhance the structural and mechanical stability of the biodegradable suture anchor. Furthermore, the results of the mechanical properties test demonstrated that the wide-tooth anchor showed superior performance with higher maximum torques and axial pull-out forces before and after corrosion. More importantly, the axial pull-out force and maximum torque for the wide-tooth anchor decreased by 5.86% and 8.64% after corrosion, which were significantly less than those for the narrow-tooth anchor. Therefore, the wide-tooth suture anchor with lower corrosion rate, higher mechanical properties and structural stability is a promising candidate for ligament-bone fixation in the repair of rotator cuff injuries

Amine-Capped Co Nanoparticles for Highly Efficient Dehydrogenation of Ammonia Borane

Highly efficient heterogeneous catalysts are desired for the development of new energy storage materials. The rational choice and use of capping ligands are of significant importance for performance optimization of metal nanoparticle (NP) catalysts. By exploiting amine-rich polyethylenimine (PEI) and graphene oxide (GO) as a NP support, we demonstrate that as a capping ligand, PEI deposited on GO provides a novel pathway able to simultaneously control the morphology, spatial distribution, surface active sites of cobalt (Co) NPs, and remarkably enhances their catalytic properties for the hydrolytic dehydrogenation of ammonia borane (AB). Such a synergistic effect enables the synthesized PEI-GO/Co catalysts to reveal extremely high dehydrogenation activities under atmosphere condition. A total turnover frequency of 39.9 mol_H2 min^–1 mol_Co^–1 and an apparent activation energy of 28.2 kJ mol^–1 make the catalytic performance of these PEI-GO/Co catalysts comparable to those of noble metal-based catalysts, including bimetallic and multimetallic catalysts